Malta

Michel Dubois, Yann Le Guyadec and Dominique Duhaut (January 1st 2009). MASL: a Language for Multi-Agent System, Multiagent Systems, Salman Ahmed and Mohd Noh Karsiti, IntechOpen, DOI: 10.5772/6604.

Wilfred Isak April, Daniel Ileni Itenge, David Namalenga and Petrus Stepfanus Erwee (September 13th 2019). Ethics and Globalisation: A Namibian Perspective - Ramatex Case, Globalization, George Yungchih Wang, IntechOpen, DOI: 10.5772/intechopen.84746. 

The main challenge that artificial intelligence research is facing nowadays is how to guarantee the development of responsible technology. And, in particular, how to guarantee that autonomy is responsible. The social fears on the actions taken by AI can only be appeased by providing ethical certification and transparency of systems. However, this is certainly not an easy task. As we very well know in the multiagent systems field, the prediction accuracy of system outcomes has limits as multiagent systems are actually examples of complex systems. And AI will be social, there will be thousands of AI systems interacting among themselves and with a multitude of humans; AI will necessarily be multiagent. Although we cannot provide complete guarantees on outcomes, we must be able to define with accuracy what autonomous behaviour is acceptable (ethical), to provide repair methods for anomalous behaviour and to explain the rationale of AI decisions. Ideally, we should be able to guarantee responsible behaviour of individual AI systems by construction. I understand by an ethical AI system one that is capable of deciding what are the most convenient norms, abide by them and make them evolve and adapt. The area of multiagent systems has developed a number of theoretical and practical tools that properly combined can provide a path to develop such systems, that is, provide means to build ethical-by-construction systems: agreement technologies to decide on acceptable ethical behaviour, normative frameworks to represent and reason on ethics, and electronic institutions to operationalise ethical interactions. Along my career, I have contributed with tools on these three areas. In this keynote, I will describe a methodology to support their combination that incorporates some new ideas from law, and organisational theory.

ICAART 2020

Responsible Agency

The AAAI 2025 presidential panel on the future of AI research aims to help all AI stakeholders navigate the recent significant transformations in AI capabilities, as well as AI research methodologies, environments, and communities. It includes 17 chapters, each covering one topic related to AI research, and sketching its history, current trends and open challenges. The study has been conducted by 25 AI researchers and supported by 15 additional contributors and 475 respondents to a community survey.

The Future of AI Research - Webinar Series

keynote

The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence. Two simultaneous related tracks will be held, covering both applications and current research work. One track focuses on Agents, Multi-Agent Systems and Software Platforms, Distributed Problem Solving and Distributed AI in general. The other track focuses mainly on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception Reactive AI Systems, and Evolutionary Computing and other topics related to Intelligent Systems and Computational Intelligence.

The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence.

How to exploit knowledge to make better use of data is a timely issue at the crossroad of knowledge representation and reasoning, data management, and the semantic web. Knowledge representation is emblematic of the symbolic approach of Artificial Intelligence based on the development of explicit logic-based models processed by generic reasoning algorithms that are founded in logic. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. Similarly to humans, computer systems can base decisions on reasoning about domain knowledge. And humans can express their data analysis needs using terms of a shared vocabulary in their domain of interest or of expertise. In this talk, I will show how reasoning on data can help to solve in a principled way several problems raised by modern data-centered applications in which data may be ubiquitous, multi-form, multi-source and musti-scale. I will also show how models and algorithms have evolved to face scalability issues and data quality challenges.

Reasoning on Data: Challenges and Application

I will summarize the findings of a recently completed, community-driven study to set research directions for AI Research for the next 20 years. Over 100 prominent AI researchers contributed to the study, which was sponsored by the US-based Computing Community Consortium (CCC), working with the National Science Foundation (NSF) and the Association for the Advancement of AI (AAAI). I co-chaired the study together with Yolanda Gil (Univ. of Southern California and AAAI President).

A 20-Year Roadmap for AI Research

When engaging in social interaction, people rely on their ability to reason about other people’s mental states, including goals, intentions, and beliefs. This theory of mind ability allows them to more easily understand, predict, and even manipulate the behavior of others. People can also use their theory of mind to reason about the theory of mind of others, which allows them to understand sentences like “Alice believes that Bob does not know that she wrote a novel under pseudonym”. But while the usefulness of higher orders of theory of mind is apparent in many social interactions, empirical evidence so far suggested that people often do not use this ability spontaneously when playing games, even when doing so would be highly beneficial. In this lecture, we discuss some experiments in which we have attempted to encourage participants to engage in higher-order theory of mind reasoning by letting them play games against computational agents: the one-shot competitive Mod game; the turn-taking game Marble Drop; and the negotiation game Colored Trails. It turns out that we can entice people to use second-order theory of mind in Marble Drop and Colored Trails, and in the Mod game even third-order theory of mind. We discuss different methods of estimating participants’ reasoning strategies in these games, some of them based only on their moves in a series of games, others based on reaction times or eye movements. In the coming era of hybrid intelligence, in which teams consist of humans, robots and software agents, it will be beneficial if the computational members of the team can diagnose the theory of mind levels of their human colleagues and even help them improve.

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